Unpaved roads are essential for transportation infrastructure, particularly for forest industry. Traditionally, unpaved roads are composed of layers using local soils. Poor local soils need to be replaced with gravel, crushed aggregate, or amixture of materials. Due to traffic and weather conditions, unpaved roads require frequent maintenance and repair. To reduce the amount of quality materials and the frequency of maintenance operations, reinforcements can be used (synthetic or natural). This paper focussed on the behaviour of a fine soil reinforced with natural fibres from the forest value chain (pine needles), to assess their use on unpaved forest roads. Cyclic CBR tests were carried out to assess the resilient response of the soil (unreinforced and reinforced); the tests included initial monotonic loading, followed by cyclic loading. The force-penetration response and CBR value improved with the inclusion of pine needles; the best response corresponded to a percentage of incorporation of 1% (mass). For the cyclic loading phase, the permanent displacement decreased with the number of cycles, approaching a resilient response. The reinforcement with pine needles led to an improved elastic response, represented by an equivalent stiffness modulus. The best behaviour was, again, obtained for a percentage of incorporation of 1% (mass). The addition of fibres led to reduced displacements during the test, relatively to the unreinforced soil. The results showed that for unpaved forest roads, where the investment in soil characterisation is often very limited, cyclic CBR tests can be a promising approach in obtaining design parameters.

The optimization of geotextile mechanical properties is crucial for enhancing their performance in civil engineering applications such as soil reinforcement and stabilization. This study focuses on the influence of manufacturing parameters on the static puncture (CBR) properties of polyester geotextiles. Polyester geotextile samples were manufactured using various parameters, including needle-punching density, penetration depth, calendering temperature, and speed. The mechanical properties of the samples, specifically strength and elongation, were evaluated using the CBR test according to EN ISO 12236. The data were analyzed using multivariate analysis of variance, followed by statistical analysis to determine the influence of the manufacturing parameters on the mechanical properties. Furthermore, the relationship between these parameters and the mechanical properties was modeled using artificial neural networks (ANN) and regression analysis. The results indicated that all manufacturing parameters significantly impacted the strength and elongation of the geotextiles. The ANN models, employing two hidden layers, predicted the strength and elongation with errors of 1.43% and 1.26%, respectively.